Saliency maps reflect that the visual attention which subjects pay for image content varies in dependency on the content depicted. Visual attention is modelled in top-down models and in bottom up models.
Top-down models are related to voluntary attention resulting from a cognitive task, such as object search, location recognition or face recognition. Bottom up models are related to involuntary attention guided only by visually relevant or stimulating areas of the image.
C. Koch and S. Ullman: “Shifts in selection in Visual Attention: Towards the Underlying Neural Circuitry”, Human Neurobiology, vol. 4, No. 4, p. 219-227, 1985, describe a model were early feature are extracted from visual input into several separate parallel channels. After this extraction and a particular treatment, a feature map is obtained for each channel. Next, the saliency map is build by fusing all these maps.
L. Itti and C. Koch: “Feature combination strategies for saliency-based visual attention systems”, JOURNAL OF ELECTRONIC IMAGING, 10 (1), p. 161-169, January 2001, study the problem of combining feature maps into a unique saliency map. Four combination strategies are compared:
(1) Simple normalized summation, (2) linear combination with learned weights, (3) global nonlinear normalization followed by summation, and (4) local nonlinear competition between salient locations followed by summation.
O. Le Meur, et al.: “A Coherent Computational Approach to Model Bottom-up Visual Attention”, IEEE Trans. On Pattern analysis and Machine intelligence, Vol 28, No. 5, p. 802-817, May 2006, propose extraction of early feature maps by a perceptual channel decomposition of each of three perceptual components by splitting the 2D spatial frequency domain both in spatial radial frequency and in orientation.
A. Torralba et al.: “Top-down control of visual attention in object detection” ICIP 2006 determines saliency maps for object search and uses scene categories for spatially tuning the saliency maps by selecting a spatial stripe and reinforcing saliency in this stripe.